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master's thesis
Upotreba logističke regresije kod osiguranja vozila
Zagreb: University of Zagreb, Faculty of Science, 2014. urn:nbn:hr:217:669587
Soldo, Marinela
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Soldo, M. (2014). Upotreba logističke regresije kod osiguranja vozila (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:669587

Soldo, Marinela. "Upotreba logističke regresije kod osiguranja vozila." Master's thesis, University of Zagreb, Faculty of Science, 2014. https://urn.nsk.hr/urn:nbn:hr:217:669587

Soldo, Marinela. "Upotreba logističke regresije kod osiguranja vozila." Master's thesis, University of Zagreb, Faculty of Science, 2014. https://urn.nsk.hr/urn:nbn:hr:217:669587

Soldo, M. (2014). 'Upotreba logističke regresije kod osiguranja vozila', Master's thesis, University of Zagreb, Faculty of Science, accessed 18 January 2025, https://urn.nsk.hr/urn:nbn:hr:217:669587

Soldo M. Upotreba logističke regresije kod osiguranja vozila [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2014 [cited 2025 January 18] Available at: https://urn.nsk.hr/urn:nbn:hr:217:669587

M. Soldo, "Upotreba logističke regresije kod osiguranja vozila", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:217:669587

Metadata
TitleUpotreba logističke regresije kod osiguranja vozila
AuthorMarinela Soldo
MentorAnamarija Jazbec (mentor)
Committee memberAnamarija Jazbec (predsjednik povjerenstva)
Committee memberDijana Ilišević (član povjerenstva)
Committee memberTina Bosner (član povjerenstva)
Committee memberMiljenko Marušić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2014-09-23, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Generalizirani linearni modeli široka su klasa vjerojatnosnih modela koji osiguravaju metode za analizu podataka koji nisu normalno distribuirani. Dopuštaju dva proširenja: distribucija u prvoj komponenti može biti bilo koja distribucija iz eksponencijalne familije, a funkcija veze može biti bilo koja monotona diferencijabilna funkcija [3]. U primjeni je često varijabla od interesa diskretna, odnosno poprima dvije ili više mogućih vrijednosti. Model logističke regresije se najčešće koristi za analizu upravo takvih podataka. U prvom poglavlju opisana je teoretska pozadina modela logističke regresije. Definirani su osnovni pojmovi te opisane metode koje koristimo za testiranje značajnosti kovarijata, prilagodbu modela i testiranje adekvatnosti modela. U drugom poglavlju opisana teoretska pozadina modela primijenjena je na aktuarske podatke korištenjem programskog paketa SAS.
Abstract (english)
Generalized linear models are a broad class of probabilistic models that provide methods for analyzing data that are not normally distributed. They allow two expansions: distribution in the first component can be any distribution of exponential families, and connection function can be any monotonic differentiable function. In appliance the variable of interest is often a discrete or acquires two or more possible values. The logistic regression is commonly used for the analysis of such data. The first chapter describes the theoretical background of logistic regression. We define the basic concepts and describe the methods for testing the significance of explanatory variables, model fitting and the adequacy of fit. The second chapter is dedicated to the application of the theoretical methods described in chapter 1 to real insurance data using the statistical software package SAS
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:669587
Study programmeTitle: Mathematical Statistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2019-02-06 15:42:33